from __future__ import absolute_import, division, print_function

import math

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from model.da_faster_rcnn_instance_da_weight.faster_rcnn import _fasterRCNN
from model.utils.config import cfg
from torch.autograd import Variable

__all__ = ["ResNet", "resnet18", "resnet34", "resnet50", "resnet101", "resnet152"]


model_urls = {
    "resnet18": "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth",
    "resnet34": "https://s3.amazonaws.com/pytorch/models/resnet34-333f7ec4.pth",
    "resnet50": "https://s3.amazonaws.com/pytorch/models/resnet50-19c8e357.pth",
    "resnet101": "https://s3.amazonaws.com/pytorch/models/resnet101-5d3b4d8f.pth",
    "resnet152": "https://s3.amazonaws.com/pytorch/models/resnet152-b121ed2d.pth",
}


def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(
        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
    )


def conv1x1(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(
        in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False
    )


class netD_pixel(nn.Module):
    def __init__(self, context=False):
        super(netD_pixel, self).__init__()
        self.conv1 = nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0, bias=False)
        self.conv2 = nn.Conv2d(256, 128, kernel_size=1, stride=1, padding=0, bias=False)
        self.conv3 = nn.Conv2d(128, 1, kernel_size=1, stride=1, padding=0, bias=False)
        self.context = context
        self._init_weights()

    def _init_weights(self):
        def normal_init(m, mean, stddev, truncated=False):
            """
        weight initalizer: truncated normal and random normal.
        """
            # x is a parameter
            if truncated:
                m.weight.data.normal_().fmod_(2).mul_(stddev).add_(
                    mean
                )  # not a perfect approximation
            else:
                m.weight.data.normal_(mean, stddev)
                # m.bias.data.zero_()

        normal_init(self.conv1, 0, 0.01)
        normal_init(self.conv2, 0, 0.01)
        normal_init(self.conv3, 0, 0.01)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        if self.context:
            feat = F.avg_pool2d(x, (x.size(2), x.size(3)))
            x = self.conv3(x)
            return F.sigmoid(x), feat
        else:
            x = self.conv3(x)
            return F.sigmoid(x)


class netD(nn.Module):
    def __init__(self, context=False):
        super(netD, self).__init__()
        self.conv1 = conv3x3(1024, 512, stride=2)
        self.bn1 = nn.BatchNorm2d(512)
        self.conv2 = conv3x3(512, 128, stride=2)
        self.bn2 = nn.BatchNorm2d(128)
        self.conv3 = conv3x3(128, 128, stride=2)
        self.bn3 = nn.BatchNorm2d(128)
        self.fc = nn.Linear(128, 2)
        self.context = context
        self.leaky_relu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

    def forward(self, x):
        x = F.dropout(F.relu(self.bn1(self.conv1(x))), training=self.training)
        x = F.dropout(F.relu(self.bn2(self.conv2(x))), training=self.training)
        x = F.dropout(F.relu(self.bn3(self.conv3(x))), training=self.training)
        x = F.avg_pool2d(x, (x.size(2), x.size(3)))
        x = x.view(-1, 128)
        if self.context:
            feat = x
        x = self.fc(x)
        if self.context:
            return x, feat
        else:
            return x


class netD_dc(nn.Module):
    def __init__(self):
        super(netD_dc, self).__init__()
        self.fc1 = nn.Linear(2048, 100)
        self.bn1 = nn.BatchNorm1d(100)
        self.fc2 = nn.Linear(100, 100)
        self.bn2 = nn.BatchNorm1d(100)
        self.fc3 = nn.Linear(100, 2)

    def forward(self, x):
        x = F.dropout(F.relu(self.bn1(self.fc1(x))), training=self.training)
        x = F.dropout(F.relu(self.bn2(self.fc2(x))), training=self.training)
        x = self.fc3(x)
        return x


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(
            inplanes, planes, kernel_size=1, stride=stride, bias=False
        )  # change
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(
            planes, planes, kernel_size=3, stride=1, padding=1, bias=False  # change
        )
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(
            kernel_size=3, stride=2, padding=0, ceil_mode=True
        )  # change
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        # it is slightly better whereas slower to set stride = 1
        # self.layer4 = self._make_layer(block, 512, layers[3], stride=1)
        self.avgpool = nn.AvgPool2d(7)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2.0 / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(
                    self.inplanes,
                    planes * block.expansion,
                    kernel_size=1,
                    stride=stride,
                    bias=False,
                ),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def resnet18(pretrained=False):
    """Constructs a ResNet-18 model.
  Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
    model = ResNet(BasicBlock, [2, 2, 2, 2])
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["resnet18"]))
    return model


def resnet34(pretrained=False):
    """Constructs a ResNet-34 model.
  Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
    model = ResNet(BasicBlock, [3, 4, 6, 3])
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["resnet34"]))
    return model


def resnet50(pretrained=False):
    """Constructs a ResNet-50 model.
  Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
    model = ResNet(Bottleneck, [3, 4, 6, 3])
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["resnet50"]))
    return model


def resnet101(pretrained=False):
    """Constructs a ResNet-101 model.
  Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
    model = ResNet(Bottleneck, [3, 4, 23, 3])
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["resnet101"]))
    return model


def resnet152(pretrained=False):
    """Constructs a ResNet-152 model.
  Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
    model = ResNet(Bottleneck, [3, 8, 36, 3])
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["resnet152"]))
    return model


class resnet(_fasterRCNN):
    def __init__(
        self,
        classes,
        num_layers=101,
        pretrained=False,
        pretrained_path=None,
        class_agnostic=False,
        lc=False,
        gc=False,
        da_use_contex=False,
    ):
        self.model_path = pretrained_path
        self.dout_base_model = 1024
        self.pretrained = pretrained
        self.class_agnostic = class_agnostic
        self.lc = lc
        self.gc = gc
        self.da_use_contex = da_use_contex
        self.layers = num_layers
        if not pretrained_path:
            self.model_path = pretrained_path
        _fasterRCNN.__init__(self, classes, class_agnostic, lc, gc, da_use_contex, 2048)

    def _init_modules(self):

        resnet = resnet101()
        if self.layers == 50:
            resnet = resnet50()
        if self.pretrained == True:
            print("Loading pretrained weights from %s" % (self.model_path))
            state_dict = torch.load(self.model_path)
            resnet.load_state_dict(
                {k: v for k, v in state_dict.items() if k in resnet.state_dict()}
            )
        # Build resnet.
        self.RCNN_base1 = nn.Sequential(
            resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
        )
        self.RCNN_base2 = nn.Sequential(resnet.layer2, resnet.layer3)
        self.netD_pixel = netD_pixel(context=self.lc)
        self.netD = netD(context=self.gc)

        self.RCNN_top = nn.Sequential(resnet.layer4)
        feat_d = 2048
        if self.lc:
            feat_d += 128
        if self.gc:
            feat_d += 128
        self.RCNN_cls_score = nn.Linear(feat_d, self.n_classes)
        if self.class_agnostic:
            self.RCNN_bbox_pred = nn.Linear(feat_d, 4)
        else:
            self.RCNN_bbox_pred = nn.Linear(feat_d, 4 * self.n_classes)

        # Fix blocks
        for p in self.RCNN_base1[0].parameters():
            p.requires_grad = False
        for p in self.RCNN_base1[1].parameters():
            p.requires_grad = False

        # assert (0 <= cfg.RESNET.FIXED_BLOCKS < 4)
        # if cfg.RESNET.FIXED_BLOCKS >= 3:
        #   for p in self.RCNN_base1[6].parameters(): p.requires_grad=False
        # if cfg.RESNET.FIXED_BLOCKS >= 2:
        #   for p in self.RCNN_base1[5].parameters(): p.requires_grad=False
        # if cfg.RESNET.FIXED_BLOCKS >= 1:
        #  for p in self.RCNN_base1[4].parameters(): p.requires_grad=False

        def set_bn_fix(m):
            classname = m.__class__.__name__
            if classname.find("BatchNorm") != -1:
                for p in m.parameters():
                    p.requires_grad = False

        self.RCNN_base1.apply(set_bn_fix)
        self.RCNN_base2.apply(set_bn_fix)
        self.RCNN_top.apply(set_bn_fix)

    def train(self, mode=True):
        # Override train so that the training mode is set as we want
        nn.Module.train(self, mode)
        if mode:
            # Set fixed blocks to be in eval mode
            self.RCNN_base1.eval()
            self.RCNN_base1[4].train()
            self.RCNN_base2.train()

            def set_bn_eval(m):
                classname = m.__class__.__name__
                if classname.find("BatchNorm") != -1:
                    m.eval()

            self.RCNN_base1.apply(set_bn_eval)
            self.RCNN_base2.apply(set_bn_eval)
            self.RCNN_top.apply(set_bn_eval)

    def _head_to_tail(self, pool5):
        fc7 = self.RCNN_top(pool5).mean(3).mean(2)
        return fc7
